A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations...

Descripción completa

Detalles Bibliográficos
Autores principales: Rocha Angulo, Rafael Augusto, Carpio, Juan Martín, Rojas-Domínguez, Alfonso, Ornelas-Rodríguez, Manuel, Puga, Héctor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297594/
http://dx.doi.org/10.1007/978-3-030-49076-8_14
Descripción
Sumario:A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.

Ejemplares similares